






"""simple docstring"""







import collections
import tempfile
import unittest

import numpy as np

from transformers.testing_utils import (
    is_pt_flax_cross_test,
    require_flax,
    require_torch,
    require_vision,
    slow,
    torch_device,
)
from transformers.utils import is_flax_available, is_torch_available, is_vision_available

from ...test_modeling_flax_common import floats_tensor, ids_tensor, random_attention_mask
from ..bert.test_modeling_flax_bert import FlaxBertModelTester
from ..clip.test_modeling_flax_clip import FlaxCLIPVisionModelTester
from ..vit.test_modeling_flax_vit import FlaxViTModelTester


if is_flax_available():
  from transformers import (
      FlaxBertModel,
      FlaxCLIPVisionModel,
      FlaxVisionTextDualEncoderModel,
      FlaxViTModel,
      VisionTextDualEncoderConfig,
      VisionTextDualEncoderProcessor,
  )
  from transformers.modeling_flax_pytorch_utils import (
      convert_pytorch_state_dict_to_flax,
      load_flax_weights_in_pytorch_model,
  )


if is_torch_available():
  import torch

  from transformers import VisionTextDualEncoderModel

if is_vision_available():
  from PIL import Image

def _a					(						_snake_case				):







      """simple docstring"""






      if isinstance(__lowerCAmelCase ,			collections.abc.Iterable				):
            return x
      return (x, x)







@require_flax
class    lowerCamelCase__   :






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          pass






    def 							_UpperCamelCase				(					self   ):
          pass






    def 							_UpperCamelCase				(					self   ):
          pass






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A   ):
          UpperCAmelCase							     =			np.abs((a - b)   ).max()
          self.assertLessEqual(_lowerCamelCase      ,_lowerCamelCase      ,F'''Difference between torch and flax is {diff} (>= {tol}).'''   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A      ,A      ,A=None      ,**A   ):
          UpperCAmelCase							     =			VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase      ,_lowerCamelCase   )

          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel(_lowerCamelCase   )

          UpperCAmelCase							     =			model(input_ids=_lowerCamelCase      ,pixel_values=_lowerCamelCase      ,attention_mask=_lowerCamelCase   )

          self.assertEqual(output["""text_embeds"""].shape      ,(input_ids.shape[0], config.projection_dim)   )
          self.assertEqual(output["""image_embeds"""].shape      ,(pixel_values.shape[0], config.projection_dim)   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A      ,A      ,A=None      ,**A   ):
          UpperCAmelCase     ,   UpperCAmelCase							     =			self.get_vision_text_model(_lowerCamelCase      ,_lowerCamelCase   )
          UpperCAmelCase							     =			{"""vision_model""": vision_model, """text_model""": text_model}
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase   )

          UpperCAmelCase							     =			model(input_ids=_lowerCamelCase      ,pixel_values=_lowerCamelCase      ,attention_mask=_lowerCamelCase   )

          self.assertEqual(output["""text_embeds"""].shape      ,(input_ids.shape[0], model.config.projection_dim)   )
          self.assertEqual(output["""image_embeds"""].shape      ,(pixel_values.shape[0], model.config.projection_dim)   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A      ,A      ,A=None      ,**A   ):
          UpperCAmelCase     ,   UpperCAmelCase							     =			self.get_vision_text_model(_lowerCamelCase      ,_lowerCamelCase   )
          UpperCAmelCase							     =			{"""vision_model""": vision_model, """text_model""": text_model}
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase   )

          UpperCAmelCase							     =			model(input_ids=_lowerCamelCase      ,pixel_values=_lowerCamelCase      ,attention_mask=_lowerCamelCase   )
          UpperCAmelCase							     =			output[0]

          with tempfile.TemporaryDirectory() as tmpdirname:
                model.save_pretrained(_lowerCamelCase   )
                UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase   )

                UpperCAmelCase							     =			model(input_ids=_lowerCamelCase      ,pixel_values=_lowerCamelCase      ,attention_mask=_lowerCamelCase   )
                UpperCAmelCase							     =			after_output[0]
                UpperCAmelCase							     =			np.amax(np.abs(out_a - out_a   )   )
                self.assertLessEqual(_lowerCamelCase      ,1e-3   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A      ,A      ,A=None      ,**A   ):
          UpperCAmelCase     ,   UpperCAmelCase							     =			self.get_vision_text_model(_lowerCamelCase      ,_lowerCamelCase   )
          UpperCAmelCase							     =			{"""vision_model""": vision_model, """text_model""": text_model}
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(**_lowerCamelCase   )

          UpperCAmelCase							     =			model(
              input_ids=_lowerCamelCase      ,pixel_values=_lowerCamelCase      ,attention_mask=_lowerCamelCase      ,output_attentions=_lowerCamelCase   )

          UpperCAmelCase							     =			output.vision_model_output.attentions
          self.assertEqual(len(_lowerCamelCase   )      ,vision_config.num_hidden_layers   )

          # in ViT, the seq_len equals the number of patches + 1 (we add 1 for the [CLS] token)
          UpperCAmelCase							     =			to_atuple(vision_model.config.image_size   )
          UpperCAmelCase							     =			to_atuple(vision_model.config.patch_size   )
          UpperCAmelCase							     =			(image_size[1] // patch_size[1]) * (image_size[0] // patch_size[0])
          UpperCAmelCase							     =			num_patches + 1
          self.assertEqual(vision_attentions[0].shape[-3:]      ,(vision_config.num_attention_heads, seq_len, seq_len)   )

          UpperCAmelCase							     =			output.text_model_output.attentions
          self.assertEqual(len(_lowerCamelCase   )      ,text_config.num_hidden_layers   )

          self.assertEqual(
              text_attentions[0].shape[-3:]      ,(text_config.num_attention_heads, input_ids.shape[-1], input_ids.shape[-1])      ,)






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A   ):
          pt_model.to(_lowerCamelCase   )
          pt_model.eval()

          # prepare inputs
          UpperCAmelCase							     =			inputs_dict
          UpperCAmelCase							     =			{k: torch.tensor(v.tolist()   ) for k, v in flax_inputs.items()}

          with torch.no_grad():
                UpperCAmelCase							     =			pt_model(**_lowerCamelCase   ).to_tuple()

          UpperCAmelCase							     =			fx_model(**_lowerCamelCase   ).to_tuple()
          self.assertEqual(len(_lowerCamelCase   )      ,len(_lowerCamelCase   )      ,"""Output lengths differ between Flax and PyTorch"""   )
          for fx_output, pt_output in zip(fx_outputs[:4]      ,pt_outputs[:4]   ):
                self.assert_almost_equals(_lowerCamelCase      ,pt_output.numpy()      ,4e-2   )

          # PT -> Flax
          with tempfile.TemporaryDirectory() as tmpdirname:
                pt_model.save_pretrained(_lowerCamelCase   )
                UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase      ,from_pt=_lowerCamelCase   )

          UpperCAmelCase							     =			fx_model_loaded(**_lowerCamelCase   ).to_tuple()
          self.assertEqual(len(_lowerCamelCase   )      ,len(_lowerCamelCase   )      ,"""Output lengths differ between Flax and PyTorch"""   )
          for fx_output_loaded, pt_output in zip(fx_outputs_loaded[:4]      ,pt_outputs[:4]   ):
                self.assert_almost_equals(_lowerCamelCase      ,pt_output.numpy()      ,4e-2   )

          # Flax -> PT
          with tempfile.TemporaryDirectory() as tmpdirname:
                fx_model.save_pretrained(_lowerCamelCase   )
                UpperCAmelCase							     =			VisionTextDualEncoderModel.from_pretrained(_lowerCamelCase      ,from_flax=_lowerCamelCase   )

          pt_model_loaded.to(_lowerCamelCase   )
          pt_model_loaded.eval()

          with torch.no_grad():
                UpperCAmelCase							     =			pt_model_loaded(**_lowerCamelCase   ).to_tuple()

          self.assertEqual(len(_lowerCamelCase   )      ,len(_lowerCamelCase   )      ,"""Output lengths differ between Flax and PyTorch"""   )
          for fx_output, pt_output_loaded in zip(fx_outputs[:4]      ,pt_outputs_loaded[:4]   ):
                self.assert_almost_equals(_lowerCamelCase      ,pt_output_loaded.numpy()      ,4e-2   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A   ):
          UpperCAmelCase							     =			VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase      ,_lowerCamelCase   )

          UpperCAmelCase							     =			VisionTextDualEncoderModel(_lowerCamelCase   )
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel(_lowerCamelCase   )

          UpperCAmelCase							     =			convert_pytorch_state_dict_to_flax(pt_model.state_dict()      ,_lowerCamelCase   )
          UpperCAmelCase							     =			fx_state

          self.check_pt_flax_equivalence(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase   )






    def 							_UpperCamelCase				(					self      ,A      ,A      ,A   ):
          UpperCAmelCase							     =			VisionTextDualEncoderConfig.from_vision_text_configs(_lowerCamelCase      ,_lowerCamelCase   )

          UpperCAmelCase							     =			VisionTextDualEncoderModel(_lowerCamelCase   )
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel(_lowerCamelCase   )

          UpperCAmelCase							     =			load_flax_weights_in_pytorch_model(_lowerCamelCase      ,fx_model.params   )

          self.check_pt_flax_equivalence(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.prepare_config_and_inputs()
          self.check_model_from_pretrained_configs(**_lowerCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.prepare_config_and_inputs()
          self.check_vision_text_dual_encoder_from_pretrained(**_lowerCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.prepare_config_and_inputs()
          self.check_save_load(**_lowerCamelCase   )






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.prepare_config_and_inputs()
          self.check_vision_text_output_attention(**_lowerCamelCase   )






    @is_pt_flax_cross_test
    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			self.prepare_config_and_inputs()
          UpperCAmelCase							     =			config_inputs_dict.pop("""vision_config"""   )
          UpperCAmelCase							     =			config_inputs_dict.pop("""text_config"""   )

          UpperCAmelCase							     =			config_inputs_dict

          self.check_equivalence_pt_to_flax(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase   )
          self.check_equivalence_flax_to_pt(_lowerCamelCase      ,_lowerCamelCase      ,_lowerCamelCase   )






    @slow
    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase     ,   UpperCAmelCase							     =			self.get_pretrained_model_and_inputs()

          UpperCAmelCase							     =			model_a(**_lowerCamelCase   )
          UpperCAmelCase							     =			outputs[0]

          with tempfile.TemporaryDirectory() as tmp_dirname:
                model_a.save_pretrained(_lowerCamelCase   )
                UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_pretrained(_lowerCamelCase   )

                UpperCAmelCase							     =			model_a(**_lowerCamelCase   )
                UpperCAmelCase							     =			after_outputs[0]
                UpperCAmelCase							     =			np.amax(np.abs(out_a - out_a   )   )
                self.assertLessEqual(_lowerCamelCase      ,1e-5   )







@require_flax
class    lowerCamelCase__   (			__lowerCAmelCase       ,	unittest.TestCase					):






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              """hf-internal-testing/tiny-random-vit"""      ,"""hf-internal-testing/tiny-bert"""      ,vision_from_pt=_lowerCamelCase      ,text_from_pt=_lowerCamelCase      ,)
          UpperCAmelCase							     =			13
          UpperCAmelCase							     =			floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ]   )
          UpperCAmelCase							     =			ids_tensor([batch_size, 4]      ,model.config.text_config.vocab_size   )
          UpperCAmelCase							     =			random_attention_mask([batch_size, 4]   )
          UpperCAmelCase							     =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

          return model, inputs






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          UpperCAmelCase							     =			FlaxViTModel(_lowerCamelCase   )
          UpperCAmelCase							     =			FlaxBertModel(_lowerCamelCase   )
          return vision_model, text_model






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxViTModelTester(self   )
          UpperCAmelCase							     =			FlaxBertModelTester(self   )
          UpperCAmelCase							     =			vit_model_tester.prepare_config_and_inputs()
          UpperCAmelCase							     =			bert_model_tester.prepare_config_and_inputs()

          UpperCAmelCase     ,   UpperCAmelCase							     =			vision_config_and_inputs

          UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			text_config_and_inputs

          # make sure that cross attention layers are added
          return {
              "text_config": text_config,
              "vision_config": vision_config,
              "pixel_values": pixel_values,
              "attention_mask": attention_mask,
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
          }







@require_torch
class    lowerCamelCase__   (			__lowerCAmelCase       ,	unittest.TestCase					):






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_vision_text_pretrained(
              """hf-internal-testing/tiny-random-clip"""      ,"""hf-internal-testing/tiny-bert"""      ,vision_from_pt=_lowerCamelCase      ,text_from_pt=_lowerCamelCase      ,)
          UpperCAmelCase							     =			13
          UpperCAmelCase							     =			floats_tensor(
              [
                  batch_size,
                  model.config.vision_config.num_channels,
                  model.config.vision_config.image_size,
                  model.config.vision_config.image_size,
              ]   )
          UpperCAmelCase							     =			ids_tensor([batch_size, 4]      ,model.config.text_config.vocab_size   )
          UpperCAmelCase							     =			random_attention_mask([batch_size, 4]   )
          UpperCAmelCase							     =			{"""pixel_values""": pixel_values, """input_ids""": input_ids, """attention_mask""": attention_mask}

          return model, inputs






    def 							_UpperCamelCase				(					self      ,A      ,A   ):
          UpperCAmelCase							     =			FlaxCLIPVisionModel(_lowerCamelCase   )
          UpperCAmelCase							     =			FlaxBertModel(_lowerCamelCase   )
          return vision_model, text_model






    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxCLIPVisionModelTester(self   )
          UpperCAmelCase							     =			FlaxBertModelTester(self   )
          UpperCAmelCase							     =			clip_model_tester.prepare_config_and_inputs()
          UpperCAmelCase							     =			bert_model_tester.prepare_config_and_inputs()

          UpperCAmelCase     ,   UpperCAmelCase							     =			vision_config_and_inputs

          UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase     ,   UpperCAmelCase							     =			text_config_and_inputs

          # make sure that cross attention layers are added
          return {
              "text_config": text_config,
              "vision_config": vision_config,
              "pixel_values": pixel_values,
              "attention_mask": attention_mask,
              "input_ids": input_ids,
              "token_type_ids": token_type_ids,
          }







@require_flax
@require_vision
class    lowerCamelCase__   (			unittest.TestCase					):






    @slow
    def 							_UpperCamelCase				(					self   ):
          UpperCAmelCase							     =			FlaxVisionTextDualEncoderModel.from_pretrained("""clip-italian/clip-italian"""      ,logit_scale_init_value=1.0   )
          UpperCAmelCase							     =			VisionTextDualEncoderProcessor.from_pretrained("""clip-italian/clip-italian"""   )

          UpperCAmelCase							     =			Image.open("""./tests/fixtures/tests_samples/COCO/000000039769.png"""   )
          UpperCAmelCase							     =			processor(
              text=["""una foto di un gatto""", """una foto di un cane"""]      ,images=_lowerCamelCase      ,padding=_lowerCamelCase      ,return_tensors="""np"""   )

          UpperCAmelCase							     =			model(**_lowerCamelCase   )

          # verify the logits
          self.assertEqual(outputs.logits_per_image.shape      ,(inputs.pixel_values.shape[0], inputs.input_ids.shape[0])   )
          self.assertEqual(
              outputs.logits_per_text.shape      ,(inputs.input_ids.shape[0], inputs.pixel_values.shape[0])      ,)

          UpperCAmelCase							     =			np.array([[1.2284727, 0.3104122]]   )

          self.assertTrue(np.allclose(outputs.logits_per_image      ,_lowerCamelCase      ,atol=1e-3   )   )




"""simple docstring"""


import random





def      lowerCamelCase_					(   __lowerCAmelCase  ,       __lowerCAmelCase  ,       __lowerCAmelCase = False							)				->   dict:




						'''simple docstring'''



						lowerCamelCase__   ={i: [] for i in range(__lowerCAmelCase							)}

						# if probability is greater or equal than 1, then generate a complete graph
						if probability >= 1:
												return complete_graph(__lowerCAmelCase							)
						# if probability is lower or equal than 0, then return a graph without edges
						if probability <= 0:
												return graph

						# for each couple of nodes, add an edge from u to v
						# if the number randomly generated is greater than probability probability
						for i in range(__lowerCAmelCase							):
												for j in range(i + 1  ,       __lowerCAmelCase							):
																		if random.random() < probability:
																								graph[i].append(__lowerCAmelCase							)
																								if not directed:
																														# if the graph is undirected, add an edge in from j to i, either
																														graph[j].append(__lowerCAmelCase							)
						return graph





def      lowerCamelCase_					(   __lowerCAmelCase							)				->   dict:




						'''simple docstring'''



						return {
						    i: [j for j in range(__lowerCAmelCase							) if i != j] for i in range(__lowerCAmelCase							)
						}


if __name__ == "__main__":
	import doctest

	doctest.testmod()


